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Record W4282935707 · doi:10.1177/20480040221102741

The burden of malnutrition & frailty in patients with coronary artery disease: An under-recognized problem

2022· article· en· W4282935707 on OpenAlexaff
Samiullah Arshad, Samina Khan, Adham Karim, Vedant Gupta

Bibliographic record

VenueJRSM Cardiovascular Disease · 2022
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsRoyal Alexandra HospitalAlberta Health Services
Fundersnot available
KeywordsMalnutritionMedicineDiseaseCoronary artery diseaseMyocardial infarctionPopulationIntensive care medicineGerontologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Elderly patients with coronary artery disease have a high prevalence of frailty and malnutrition. Frailty syndrome is associated with poor outcomes in patients with myocardial infarction. There is a known overlap between frailty and malnutrition, yet these are two different entities. Fried Frailty Phenotype, Frail Scale, timed up and go test, and gait speed are rapid screening tests that may identify patients with frailty in everyday clinical setting. Short Form MNA is a sensitive tool to screen for malnutrition. Despite the availability of several tools for screening for both these conditions, the screening rates remain low. We aim to create awareness about the impacts of frailty and malnutrition, provide a brief overview of tools available and highlight the importance of screening in this high-risk population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.235
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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